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发明公开
CN103955755A 采用复合数据源基于自学习多项式核函数支持向量机的风电功率短期预测方法 无效 - 驳回

采用复合数据源基于自学习多项式核函数支持向量机的风电功率短期预测方法
摘要：
本发明公开了采用复合数据源基于自学习多项式核函数支持向量机的风电功率短期预测方法，主要包括：采用基于自学习多项式核函数支持向量机的复合数据源，对待测风电功率预测模型进行训练；基于待测风电功率的预测模型训练结构，对待测风电功率进行短期预测。本发明所述采用复合数据源基于自学习多项式核函数支持向量机的风电功率短期预测方法，可以克服现有技术中风电功率短期预测精度低等缺陷，以实现高精度的风电功率短期预测的优点。
摘要（英）：

The invention discloses a wind electricity power short-term prediction method by adopting a composite data source based on a self-learning polynomial kernel function support vector machine. The method mainly comprises the steps of adopting the composite data source based on the self-learning polynomial kernel function support vector machine, carrying out model training on wind electricity power to be detected, and carrying out short-term prediction on the wind electricity power to be detected on the basis of the result of the model training of the wind electricity power to be detected. The wind electricity power short-term prediction method by adopting the composite data source based on the self-learning polynomial kernel function support vector machine can overcome the defect that the prediction precision of the wind electricity power is low in the prior art, and has the advantages of achieving high-precision wind electricity power short-term prediction.

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